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Table S1. Mutational status of all specimens regarding KRAS and EGFR.

Legends for Supplementary Figures and Tables

Figure S1. Cross-docking of sunitinib and dasatinib in NQO2 and PDXK.

A growing number of gene mutations, which are recognized as cancer drivers, can be successfully targeted with drugs. The redundant and dynamic nature of oncogenic signaling networks and complex interactions between cancer cells and the microenvironment, however, can cause drug resistance. While these challenges can be addressed by developing drug combinations or polypharmacology drugs, this benefits greatly from a detailed understanding of the proteome-wide target profiles. Using mass spectrometry-based chemical proteomics, we report the comprehensive characterization of the drug–protein interaction networks for the multikinase inhibitors dasatinib and sunitinib in primary lung cancer tissue specimens derived from patients. We observed in excess of 100 protein kinase targets plus various protein complexes involving, for instance, AMPK, TBK1 (sunitinib), and ILK (dasatinib). Importantly, comparison with lung cancer cell lines and mouse xenografts thereof showed that most targets were shared between cell lines and tissues. Several targets, however, were only present in tumor tissues. In xenografts, most of these proteins were of mouse origin suggesting that they originate from the tumor microenvironment. Furthermore, intersection with subsequent global phosphoproteomic analysis identified several activated signaling pathways. These included MAPK, immune, and integrin signaling, which were affected by these drugs in both cancer cells and the microenvironment. Thus, the combination of chemical and phosphoproteomics can generate a systems view of proteins, complexes, and signaling pathways that are simultaneously engaged by multitargeted drugs in cancer cells and the tumor microenvironment. This may allow for the design of novel anticancer therapies that concurrently target multiple tumor compartments. Mol Cancer Ther; 13(11); 2751–62. ©2014 AACR.

Antimicrobial resistance (AMR) is a public health threat where efficient surveillance is needed to prevent outbreaks. Existing methods for detection of gastrointestinal colonization of multidrug-resistant organisms (MDRO) are limited to specific organisms or resistance mechanisms. Metagenomic next-generation sequencing (mNGS) is a more rapid and agnostic diagnostic approach for microbiome and resistome investigations. We determined if mNGS can detect MDRO from rectal swabs in concordance with standard microbiology results. We performed and compared mNGS performance on short-read Illumina MiSeq (N = 10) and long-read Nanopore MinION (N = 5) platforms directly from rectal swabs to detect vancomycin-resistant enterococci (VRE) and carbapenem-resistant Gram-negative organisms (CRO). We detected Enterococcus faecium (N = 8) and Enterococcus faecalis (N = 2) with associated van genes (9/10) in concordance with VRE culture-based results. We studied the microbiome and identified CRO, Pseudomonas aeruginosa (N = 1), Enterobacter cloacae (N = 1), and KPC-producing Klebsiella pneumoniae (N = 1). Nanopore real-time analysis detected the blaKPC gene in 2.3 min and provided genetic context (blaKPC harbored on pKPC_Kp46 IncF plasmid). Illumina sequencing provided accurate allelic variant determination (i.e., blaKPC-2) and strain typing of the K. pneumoniae (ST-15). We demonstrated an agnostic approach for surveillance of MDRO, examining advantages of both short- and long-read mNGS methods for AMR detection.

Summary MMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contig’s taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2–18× faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments. Availability and implementation MMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https://mmseqs.com. Supplementary information Supplementary data are available at Bioinformatics online.

Metagenomic experiments expose the wide range of microscopic organisms in any microbial environment through high-throughput DNA sequencing. The computational analysis of the sequencing data is critical for the accurate and complete characterization of the microbial community. To facilitate efficient and reproducible metagenomic analysis, we introduce a step-by-step protocol for the Kraken suite, an end-to-end pipeline for the classification, quantification and visualization of metagenomic datasets. Our protocol describes the execution of the Kraken programs, via a sequence of easy-to-use scripts, in two scenarios: (1) quantification of the species in a given metagenomics sample; and (2) detection of a pathogenic agent from a clinical sample taken from a human patient. The protocol, which is executed within 1-2 h, is targeted to biologists and clinicians working in microbiome or metagenomics analysis who are familiar with the Unix command-line environment.

PurposeAntimicrobial resistance (AMR) is a public health threat where efficient surveillance is needed to prevent outbreaks. Existing methods for detection of gastrointestinal colonization of multidrug-resistant organisms (MDRO) are limited to specific organisms or resistance mechanisms. Metagenomic next-generation sequencing (mNGS) is a more rapid and agnostic diagnostic approach for microbiome and resistome investigations. We determined if mNGS can detect MDRO from rectal swabs in concordance with standard microbiology results.MethodsWe performed and compared mNGS performance on short-read Illumina MiSeq (N=10) and long-read Nanopore MinION (N=4) platforms directly from peri-rectal swabs to detect vancomycin-resistant enterococci (VRE) and carbapenem-resistant Gram-negative organisms (CRO).ResultsWe detected E. faecium (N=8) and E. faecalis (N=2) with associated van genes (9/10) in concordance with VRE culture-based results. We studied the microbiome and identified CRO organisms, P. aeruginosa (N=1), E. cloacae (N=1), and KPC-producing K. pneumoniae (N=1). Nanopore real-time detection detected the blaKPC gene in 2.5 minutes and provided genetic context (blaKPC harbored on pKPC_Kp46 IncF plasmid). Illumina sequencing provided accurate allelic variant determination (i.e., blaKPC-2) and strain typing of the K. pneumoniae (ST-15). Conclusions: We demonstrated an agnostic approach for surveillance of MDRO, examining advantages of both short and long-read mNGS methods for AMR detection.

Antimicrobial resistance (AMR) is a public health threat where efficient surveillance is needed to prevent outbreaks. Existing methods for detection of gastrointestinal colonization of multidrug-resistant organisms (MDRO) are limited to specific organisms or resistance mechanisms. Metagenomic next-generation sequencing (mNGS) is a more rapid and agnostic diagnostic approach for microbiome and resistome investigations. We determined if mNGS can detect MDRO from rectal swabs in concordance with standard microbiology results. We performed and compared mNGS performance on short-read Illumina MiSeq (N = 10) and long-read Nanopore MinION (N = 5) platforms directly from rectal swabs to detect vancomycin-resistant enterococci (VRE) and carbapenem-resistant Gram-negative organisms (CRO). We detected Enterococcus faecium (N = 8) and Enterococcus faecalis (N = 2) with associated van genes (9/10) in concordance with VRE culture-based results. We studied the microbiome and identified CRO, Pseudomonas aeruginosa (N = 1), Enterobacter cloacae (N = 1), and KPC-producing Klebsiella pneumoniae (N = 1). Nanopore real-time analysis detected the bla

Background Even when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates. Results We built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth. Conclusions Domain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.

Background The past decade of microbiome research has concentrated on cataloging the diversity of taxa in different environments. The next decade is poised to focus on microbial traits and function. Most existing methods for doing this perform pathway analysis using reference databases. This has both benefits and drawbacks. Function can go undetected if reference databases are coarse-grained or incomplete. Likewise, detection of a pathway does not guarantee expression of the associated function. Finally, function cannot be connected to specific microbial constituents, making it difficult to ascertain the types of organisms exhibiting particular traits—something that is important for understanding microbial success in specific environments. A complementary approach to pathway analysis is to use the wealth of microbial trait information collected over years of lab-based, culture experiments. Methods Here, we use journal articles and Bergey’s Manual of Systematic Bacteriology to develop a trait-based database for 971 human skin bacterial taxa. We then use this database to examine functional traits that are over/underrepresented among skin taxa. Specifically, we focus on three trait classes—binary, categorical, and quantitative—and compare trait values among skin taxa and microbial taxa more broadly. We compare binary traits using a Chi-square test, categorical traits using randomization trials, and quantitative traits using a nonparametric relative effects test based on global rankings using Tukey contrasts. Results We find a number of traits that are over/underrepresented within the human skin microbiome. For example, spore formation, acid phosphatase, alkaline phosphatase, pigment production, catalase, and oxidase are all less common among skin taxa. As well, skin bacteria are less likely to be aerobic, favoring, instead, a facultative strategy. They are also less likely to exhibit gliding motility, less likely to be spirillum or rod-shaped, and less likely to grow in chains. Finally, skin bacteria have more difficulty at high pH, prefer warmer temperatures, and are much less resilient to hypotonic conditions. Conclusions Our analysis shows how an approach that relies on information from culture experiments can both support findings from pathway analysis, and also generate new insights into the structuring principles of microbial communities.

We assembled the sequences from deep RNA sequencing experiments by the Genotype-Tissue Expression (GTEx) project, to create a new catalog of human genes and transcripts, called CHESS. The new database contains 42,611 genes, of which 20,352 are potentially protein-coding and 22,259 are noncoding, and a total of 323,258 transcripts. These include 224 novel protein-coding genes and 116,156 novel transcripts. We detected over 30 million additional transcripts at more than 650,000 genomic loci, nearly all of which are likely nonfunctional, revealing a heretofore unappreciated amount of transcriptional noise in human cells. The CHESS database is available at http://ccb.jhu.edu/chess .

Contaminant sequences that appear in published genomes can cause numerous problems for downstream analyses, particularly for evolutionary studies and metagenomics projects. Our large-scale scan of complete and draft bacterial and archaeal genomes in the NCBI RefSeq database reveals that 2250 genomes are contaminated by human sequence. The contaminant sequences derive primarily from high-copy human repeat regions, which themselves are not adequately represented in the current human reference genome, GRCh38. The absence of the sequences from the human assembly offers a likely explanation for their presence in bacterial assemblies. In some cases, the contaminating contigs have been erroneously annotated as containing protein-coding sequences, which over time have propagated to create spurious protein “families” across multiple prokaryotic and eukaryotic genomes. As a result, 3437 spurious protein entries are currently present in the widely used nr and TrEMBL protein databases. We report here an extensive list of contaminant sequences in bacterial genome assemblies and the proteins associated with them. We found that nearly all contaminants occurred in small contigs in draft genomes, which suggests that filtering out small contigs from draft genome assemblies may mitigate the issue of contamination while still keeping nearly all of the genuine genomic sequences.

The ability to comprehensively characterize exposures and immune responses to viral infections will be critical to better understanding human health and disease. We previously described the VirScan system, a phage-display based technology for profiling antibody binding to a comprehensive library of peptides designed to represent the human virome. The previous VirScan analytical approach did not fully account for disproportionate representation of viruses in the library or for antibody cross-reactivity among sequences shared by related viruses. Here we present the ‘AntiViral Antibody Response Deconvolution Algorithm’ (‘AVARDA’), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection at species-level resolution by considering alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. By comparing acute and convalescent sera, AVARDA successfully confirmed or detected antibody responses to human herpesviruses 1, 3, 4, 5, and 6, thereby improving the rate of diagnosing viral encephalitis in this cohort by 62.5%. We further assessed AVARDA’s utility in the setting of an epidemiological study, demonstrating its ability to determine infections acquired in a child followed prospectively from infancy. We consider ways in which AVARDA’s conceptual framework may be further developed in the future and describe how its analyses may be extended beyond investigations of viral infection. AVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases.

False-positive identifications are a significant problem in metagenomics classification. We present KrakenUniq, a novel metagenomics classifier that combines the fast k-mer-based classification of Kraken with an efficient algorithm for assessing the coverage of unique k-mers found in each species in a dataset. On various test datasets, KrakenUniq gives better recall and precision than other methods and effectively classifies and distinguishes pathogens with low abundance from false positives in infectious disease samples. By using the probabilistic cardinality estimator HyperLogLog, KrakenUniq runs as fast as Kraken and requires little additional memory. KrakenUniq is freely available at https://github.com/fbreitwieser/krakenuniq.

The purpose of this study was to develop and optimize different processing, extraction, amplification, and sequencing methods for metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) specimens. We applied mNGS to 10 CSF samples with known standard-of-care testing (SoC) results (8 positive and 2 negative). Each sample was subjected to nine different methods by varying the sample processing protocols (supernatant, pellet, neat CSF), sample pretreatment (with or without bead beating), and the requirement of nucleic acid amplification steps using DNA sequencing (DNASeq) (with or without whole-genome amplification [WGA]) and RNA sequencing (RNASeq) methods. Negative extraction controls (NECs) were used for each method variation (4/CSF sample). Host depletion (HD) was performed on a subset of samples. We correctly determined the pathogen in 7 of 8 positive samples by mNGS compared to SoC. The two negative samples were correctly interpreted as negative. The processing protocol applied to neat CSF specimens was found to be the most successful technique for all pathogen types. While bead beating introduced bias, we found it increased the detection yield of certain organism groups. WGA prior to DNASeq was beneficial for defining pathogens at the positive threshold, and a combined DNA and RNA approach yielded results with a higher confidence when detected by both methods. HD was required for detection of a low-level-positive enterovirus sample. We demonstrate that NECs are required for interpretation of these complex results and that it is important to understand the common contaminants introduced during mNGS. Optimizing mNGS requires the use of a combination of techniques to achieve the most sensitive, agnostic approach that nonetheless may be less sensitive than SoC tools.

We identified two genes induced by type 1 interferon in activated CD4+ T cells that are associated with HIV restriction in humans., Type 1 interferons (IFN) are critical for host control of HIV and simian immunodeficiency virus. However, it is unknown which of the hundreds of interferon-stimulated genes (ISGs) restrict HIV in vivo. We sequenced RNA from cells that support HIV replication (activated CD4+ T cells) in 19 HIV-infected people before and after interferon-α2b (IFN-α2b) injection. IFN-α2b administration reduced plasma HIV RNA and induced mRNA expression in activated CD4+ T cells: The IFN-α2b–induced change of each mRNA was compared to the change in plasma HIV RNA. Of 99 ISGs, 13 were associated in magnitude with plasma HIV RNA decline. In addition to well-known restriction factors among the 13 ISGs, two novel genes, CMPK2 and BCL-G, were identified and confirmed for their ability to restrict HIV in vitro: The effect of IFN on HIV restriction in culture was attenuated with RNA interference to CMPK2, and overexpression of BCL-G diminished HIV replication. These studies reveal novel antiviral molecules that are linked with IFN-mediated restriction of HIV in humans.

Purpose We test the ability of next-generation sequencing, combined with computational analysis, to identify a range of organisms causing infectious keratitis. Methods This retrospective study evaluated 16 cases of infectious keratitis and four control corneas in formalin-fixed tissues from the pathology laboratory. Infectious cases also were analyzed in the microbiology laboratory using culture, polymerase chain reaction, and direct staining. Classified sequence reads were analyzed with two different metagenomics classification engines, Kraken and Centrifuge, and visualized using the Pavian software tool. Results Sequencing generated 20 to 46 million reads per sample. On average, 96% of the reads were classified as human, 0.3% corresponded to known vectors or contaminant sequences, 1.7% represented microbial sequences, and 2.4% could not be classified. The two computational strategies successfully identified the fungal, bacterial, and amoebal pathogens in most patients, including all four bacterial and mycobacterial cases, five of six fungal cases, three of three Acanthamoeba cases, and one of three herpetic keratitis cases. In several cases, additional potential pathogens also were identified. In one case with cytomegalovirus identified by Kraken and Centrifuge, the virus was confirmed by direct testing, while two where Staphylococcus aureus or cytomegalovirus were identified by Centrifuge but not Kraken could not be confirmed. Confirmation was not attempted for an additional three potential pathogens identified by Kraken and 11 identified by Centrifuge. Conclusions Next generation sequencing combined with computational analysis can identify a wide range of pathogens in formalin-fixed corneal specimens, with potential applications in clinical diagnostics and research.

Summary Pavian is a web application for exploring classification results from metagenomics experiments. With Pavian, researchers can analyze, visualize and transform results from various classifiers—such as Kraken, Centrifuge and MethaPhlAn—using interactive data tables, heatmaps and Sankey flow diagrams. An interactive alignment coverage viewer can help in the validation of matches to a particular genome, which can be crucial when using metagenomics experiments for pathogen detection. Availability and implementation Pavian is implemented in the R language as a modular Shiny web app and is freely available under GPL-3 from http://github.com/fbreitwieser/pavian.

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (

Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets. Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review some of the challenges facing these methods. All of the methods rely on public genome databases, and we also discuss the content of these databases and how their quality has a direct impact on our ability to interpret a microbiome sample.

OBJECTIVE To assess if the reduction in HIV-1 RNA in CD4 T cells is correlated with the persistence of immune activation following early antiretroviral therapy (ART). DESIGN Clinical trial (NCT01285050). METHODS Next-generation sequencing was used to study total RNA from activated CD4 T cells (CD38 and human leukocyte antigen - antigen D related (HLA-DR) expressing) collected from 19 treatment-naive HIV-1/hepatitis C virus-infected patients before and early after ART initiation (≥12 weeks after plasma HIV-1 RNA

Background Next-generation metagenomic sequencing (NGMS) has opened new frontiers in microbial discovery but has been clinically characterized in only a few settings. Objective To explore the plasma virome of persons who inject drugs and to characterize the sensitivity and accuracy of NGMS compared with quantitative clinical standards. Design Longitudinal and cross-sectional studies. Setting A clinical trial (ClinicalTrials.gov: NCT01285050) and a well-characterized cohort study of persons who have injected drugs. Participants Persons co-infected with hepatitis C virus (HCV) and HIV. Measurements Viral nucleic acid in plasma by NGMS and quantitative polymerase chain reaction (PCR). Results Next-generation metagenomic sequencing generated a total of 600 million reads, which included the expected HIV and HCV RNA sequences. HIV and HCV reads were consistently identified only when samples contained more than 10 000 copies/mL or IU/mL, respectively, as determined by quantitative PCR. A novel RNA virus, human hepegivirus-1 (HHpgV-1), was also detected by NGMS in 4 samples from 2 persons in the clinical trial. Through use of a quantitative PCR assay for HHpgV-1, infection was also detected in 17 (10.9%) of 156 members of a cohort of persons who injected drugs. In these persons, HHpgV-1 viremia persisted for a median of at least 4538 days and was associated with detection of other bloodborne viruses, such as HCV RNA and SEN virus D. Limitation The medical importance of HHpgV-1 infection is unknown. Conclusion Although NGMS is insensitive for detection of viruses with relatively low plasma nucleic acid concentrations, it may have broad potential for discovery of new viral infections of possible medical importance, such as HHpgV-1. Primary funding source National Institutes of Health.

Centrifuge is a novel microbial classification engine that enables rapid, accurate and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4,078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI non-redundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer based indexing schemes, which require far more extensive space. Centrifuge is available as free, open-source software from www.ccb.jhu.edu/software/centrifuge

SummaryPavian is a web application for exploring metagenomics classification results, with a special focus on infectious disease diagnosis. Pinpointing pathogens in metagenomics classification results is often complicated by host and laboratory contaminants as well as many non-pathogenic microbiota. With Pavian, researchers can analyze, display and transform results from the Kraken and Centrifuge classifiers using interactive tables, heatmaps and flow diagrams. Pavian also provides an alignment viewer for validation of matches to a particular genome.Availability and implementationPavian is implemented in the R language and based on the Shiny framework. It can be hosted on Windows, Mac OS X and Linux systems, and used with any contemporary web browser. It is freely available under a GPL-3 license from http://github.com/fbreitwieser/pavian. Furthermore a Docker image is provided at https://hub.docker.com/r/florianbw/pavian.Contactfbreitw1@jhu.eduSupplementary informationSupplementary data is available at Bioinformatics online.

The purpose of the study was to perform a detailed, quantitative protein analysis of the aqueous (AF) and vitreous fluids (VF) from human eyes with idiopathic epiretinal membranes (iERM). The complementary approaches of quantitative liquid chromatography mass spectrometry (LCMS) and multiplex protein assays were utilised to reveal the protein composition in ocular fluids of this retinal disorder. In a prospective clinical trial, AF and VF was collected during surgery from twenty four eyes corresponding to twenty four patients with iERM. VF and AF from eight patients were labelled with the 4-plex iTRAQ reagent and analysed by LCMS. Each iTRAQ 4-plex experiment consisted of the AH and VH from two patients. A total of 323 proteins were identified in the AF and VF from eyes with iERM. Grouping the proteins according to involvement in biological processes, showed that the majority were involved in the classical and alternative pathway of complement activation (n = 27), proteolysis (n = 26) and cell adhesion (n = 28). iTRAQ relative quantitation revealed minimal variation in the protein content between both ocular compartments with only 3.96% of the identified proteins significantly, differentially-expressed. Eight proteins were expressed at a higher level in the VF compared to the AF; and 4 proteins were expressed at a lower level in the VF compared to the AF. For the multiplex bead assays, 29 growth factors and cytokines were assessed in the AF and VF from 16 additional patients with iERM. The protein profile was shown to be similar between VF and AF for the majority of factors except PDGF-AA. This factor was expressed at a higher level in the VF compared to the AF. The data presented in this study revealed that the majority of the proteins identified by LCMS and multiplex bead assays were present in both ocular compartments in similar quantities. This study is a first step, ‘discovery phase’ towards revealing and quantitating the protein content in the aqueous and vitreous fluid in human eyes with iERM.

Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomal RNA gene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.

Occurrence of the BCR-ABL(T315I) gatekeeper mutation is among the most pressing challenges in the therapy of chronic myeloid leukemia (CML). Several BCR-ABL inhibitors have multiple targets and pleiotropic effects that could be exploited for their synergistic potential. Testing combinations of such kinase inhibitors identified a strong synergy between danusertib and bosutinib that exclusively affected CML cells harboring BCR-ABL(T315I). To elucidate the underlying mechanisms, we applied a systems-level approach comprising phosphoproteomics, transcriptomics and chemical proteomics. Data integration revealed that both compounds targeted Mapk pathways downstream of BCR-ABL, resulting in impaired activity of c-Myc. Using pharmacological validation, we assessed that the relative contributions of danusertib and bosutinib could be mimicked individually by Mapk inhibitors and collectively by downregulation of c-Myc through Brd4 inhibition. Thus, integration of genome- and proteome-wide technologies enabled the elucidation of the mechanism by which a new drug synergy targets the dependency of BCR-ABL(T315I) CML cells on c-Myc through nonobvious off targets.

Aberrations in skin morphology and functionality can cause acute and chronic skin-related diseases that are the focus of dermatological research. Mechanically induced skin suction blister fluid may serve as a potential, alternative human body fluid for quantitative mass spectrometry (MS)-based proteomics in order to assist in the understanding of the mechanisms and causes underlying skin-related diseases. The combination of abundant-protein removal with iTRAQ technology and multidimensional fractionation techniques improved the number of identified protein groups. A relative comparison of a cohort of 8 healthy volunteers was thus recruited in order to assess the net variability encountered in a healthy scenario. The technology enabled the identification, to date, of the highest number of reported protein groups (739) with concomitant relative quantitative data for over 90% of all proteins with high reproducibility and accuracy. The use of iTRAQ 8-plex resulted in a 66% decrease in protein identifications but, despite this, provided valuable insight into interindividual differences of the healthy control samples. The geometric mean ratio was close to 1 with 95% of all ratios ranging between 0.45 and 2.05 and a calculated mean coefficient of variation of 15.8%, indicating a lower biological variance than that reported for plasma or urine. By applying a multistep sample processing, the obtained sensitivity and accuracy of quantitative MS analysis demonstrates the prospective value of the approach in future research into skin diseases.

In this study, we report a novel use for the iTRAQ reagent combined with a peptide mass inclusion list to enhance the signal of low-abundance proteins during analysis by mass spectrometry. C-tagged-SH-EGFR was retrovirally transduced into two mutant lung cancer cell lines (HCC827 and PC9), and the core protein complexes were enriched by tandem affinity purification. Tryptically digested peptides were derivatized with iTRAQ and analyzed by higher-energy collision-induced dissociation mass spectrometry. The data revealed that UBS3B is a member of the EGFR core complex in the HCC827 cell line, which was not apparent by standard, unbiased one-dimensional shotgun analysis and collision-induced dissociation. The expression level of UBS3B, however, was 6-10 times lower than that observed in the PC9 cell line. Thus, using iTRAQ in this fashion allows the identification of low-abundance interactors when combined with samples where the same protein has a higher abundance. Ultimately, this approach may uncover proteins that were previously unknown or only suspected as members of core protein complexes.

MASPECTRAS 2 is a freely available platform for integrating MS protein identifications with information from the major bioinformatics databases (ontologies, domains, literature, etc.). It assists researchers in understanding their data and publishing through sample comparisons, targeted queries, summaries, and exports in multiple formats such as PRIDE XML (Jones et al., Nucleic Acids Res. 2008, 36, D878-D883). MASPECTRAS 2 also comprises mechanisms to facilitate its integration in a high-throughput infrastructure. We illustrate application of MASPECTRAS 2 with unpublished tyrosine kinase inhibitor drug proteomics profiles in cancerous cells.

Metagenomic sequence data can be used to detect the presence of infectious viruses and bacteria, but normal microbial flora make this process challenging. We re-analyzed metagenomic RNA sequence data collected during a recent outbreak of acute flaccid myelitis (AFM), caused in some cases by infection with enterovirus D68. We found that among the patients whose symptoms were previously attributed to enterovirus D68, one patient had clear evidence of infection with Haemophilus influenzae, and a second patient had a severe Staphylococcus aureus infection caused by a methicillin-resistant strain. Neither of these bacteria were identified in the original study. These observations may have relevance in cases that present with flaccid paralysis because bacterial infections, co-infections or post-infection immune responses may trigger pathogenic processes that may present as poliomyelitis-like syndromes and may mimic AFM. A separate finding was that large numbers of human sequences were present in each of the publicly released samples, although the original study reported that human sequences had been removed before deposition.

Summary Lipid metabolism and receptor-mediated signaling are highly intertwined processes that cooperate to fulfill cellular functions and safeguard cellular homeostasis. Activation of Toll-like receptors (TLRs) leads to a complex cellular response, orchestrating a diverse range of inflammatory events that need to be tightly controlled. Here, we identified the GPI-anchored Sphingomyelin Phosphodiesterase, Acid-Like 3B (SMPDL3B) in a mass spectrometry screening campaign for membrane proteins co-purifying with TLRs. Deficiency of Smpdl3b in macrophages enhanced responsiveness to TLR stimulation and profoundly changed the cellular lipid composition and membrane fluidity. Increased cellular responses could be reverted by re-introducing affected ceramides, functionally linking membrane lipid composition and innate immune signaling. Finally, Smpdl3b-deficient mice displayed an intensified inflammatory response in TLR-dependent peritonitis models, establishing its negative regulatory role in vivo. Taken together, our results identify the membrane-modulating enzyme SMPDL3B as a negative regulator of TLR signaling that functions at the interface of membrane biology and innate immunity., Graphical Abstract, Highlights • Identification of SMPDL3B as lipid-modulating phosphodiesterase on macrophages • Negative regulatory role for SMPDL3B in Toll-like receptor function • Strong influence of SMPDL3B on membrane lipid composition and fluidity • Smpdl3b-deficient mice show enhanced responsiveness in TLR-dependent peritonitis, Heinz et al. identify the lipid-modulating phosphodiesterase SMPDL3B as negative regulator of Toll-like receptor function. Smpdl3b-deficiency strongly affected macrophage lipid composition and fluidity and led to higher responsiveness to TLR stimulation. Peritonitis models in Smpdl3b-deficient mice confirmed the negative regulatory role in vivo.

A growing number of gene mutations, which are recognized as cancer drivers, can be successfully targeted with drugs. The redundant and dynamic nature of oncogenic signaling networks and complex interactions between cancer cells and the microenvironment, however, can cause drug resistance. While these challenges can be addressed by developing drug combinations or polypharmacology drugs, this benefits greatly from a detailed understanding of the proteome-wide target profiles. Using mass spectrometry-based chemical proteomics, we report the comprehensive characterization of the drug–protein interaction networks for the multikinase inhibitors dasatinib and sunitinib in primary lung cancer tissue specimens derived from patients. We observed in excess of 100 protein kinase targets plus various protein complexes involving, for instance, AMPK, TBK1 (sunitinib), and ILK (dasatinib). Importantly, comparison with lung cancer cell lines and mouse xenografts thereof showed that most targets were shared between cell lines and tissues. Several targets, however, were only present in tumor tissues. In xenografts, most of these proteins were of mouse origin suggesting that they originate from the tumor microenvironment. Furthermore, intersection with subsequent global phosphoproteomic analysis identified several activated signaling pathways. These included MAPK, immune, and integrin signaling, which were affected by these drugs in both cancer cells and the microenvironment. Thus, the combination of chemical and phosphoproteomics can generate a systems view of proteins, complexes, and signaling pathways that are simultaneously engaged by multitargeted drugs in cancer cells and the tumor microenvironment. This may allow for the design of novel anticancer therapies that concurrently target multiple tumor compartments. Mol Cancer Ther; 13(11); 2751–62. ©2014 AACR.

International audience; Molecular mechanisms that maintain lineage integrity of helper T cells are largely unknown. Here we show histone deacetylases 1 and 2 (HDAC1 and HDAC2) as crucial regulators of this process. Loss of HDAC1 and HDAC2 during late T cell development led to the appearance of major histocompatibility complex (MHC) class II-selected CD4(+) helper T cells that expressed CD8-lineage genes such as Cd8a and Cd8b1. HDAC1 and HDAC2-deficient T helper type 0 (TH0) and TH1 cells further upregulated CD8-lineage genes and acquired a CD8(+) effector T cell program in a manner dependent on Runx-CBFbeta complexes, whereas TH2 cells repressed features of the CD8(+) lineage independently of HDAC1 and HDAC2. These results demonstrate that HDAC1 and HDAC2 maintain integrity of the CD4 lineage by repressing Runx-CBFbeta complexes that otherwise induce a CD8(+) effector T cell-like program in CD4(+) T cells.

International audience; Melanoma, the deadliest form of skin cancer, is highly immunogenic and frequently infiltrated with immune cells including B cells. The role of tumor-infiltrating B cells (TIBCs) in melanoma is as yet unresolved, possibly due to technical challenges in obtaining TIBCs in sufficient quantity for extensive studies and due to the limited life span of B cells in vitro. A comprehensive workflow has thus been developed for successful isolation and proteomic analysis of a low number of TIBCs from fresh, human melanoma tissue. In addition, we generated in vitro-proliferating TIBC cultures using simultaneous stimulation with Epstein-Barr virus (EBV) and the TLR9 ligand CpG-oligodesoxynucleotide (CpG ODN). The FASP method and iTRAQ labeling were utilized to obtain a comparative, semiquantitative proteome to assess EBV-induced changes in TIBCs. By using as few as 100 000 B cells ( approximately 5 mug protein)/sample for our proteomic study, a total number of 6507 proteins were identified. EBV-induced changes in TIBCs are similar to those already reported for peripheral B cells and largely involve changes in cell cycle proliferation, apoptosis, and interferon response, while most of the proteins were not significantly altered. This study provides an essential, further step toward detailed characterization of TIBCs including functional in vitro analysis.